Computing resource monitoring systems have evolved and continue to evolve to keep up with the demands of the organizations that use them. Many organizations, for example, utilize computing resource monitoring systems for, among other reasons, evaluating one or more metrics associated with resources and applications that the organizations may utilize to support their businesses. Despite their many advantages, many modern computing resource monitoring systems are prone to data corruption or defects resulting in potential data loss. For example, if data undergoes an irreversible transformation and the transformed data includes one or more defects, it may be difficult or impossible to reverse the process. Currently, many modern computing resource monitoring systems utilize a horizontal partition to store one or more metrics that, if lost, may result in any metrics that reside within the partition being unavailable for all reads and writes at any point in time. Additionally, these modern computing resource monitoring systems may be configured to modify or delete older data objects as new data is introduced. This, in turn, could exacerbate the damage resulting from data corruption or defective transformation of data. Adequately addressing these issues, such as through provisioning additional resources to adequately provide data redundancy in the event of data corruption or irreversible defective transformation, presents additional costs to the organizations that rely on the metric monitoring systems and the computing resource service provider that may provide the metric monitoring systems to its customers.